Normality Test of the Water Quality Monitoring Data in Harbour

摘要

Normality test (hereafter NT) is a highly recommended test for statistical estimation because the normality assumption on the data is the basic and essential.NT was carried using the KOEM water quality monitoring data in harbor which are composed of total 3,000 data sets (50 stations,30 water quality parameters including surface and bottom layers,and two seasons,such as summer and winter).The comparative analysis of the normality are carried out using total 18 methods supported by the R program packages.In addition,the Shapiro-Wilk test method is selected as the references method in this study for the analysis on the data transformation and outliers’s effects in detail.The numbers of normality assumption rejection (NAR) are estimated and compared to these cases,before and after applications of the Box-Cox transformation and Rosner’s outlier test.The NAR numbers are reduced from 24-28 to 3-4 in the “before and after” BC transformation cases with the no outlier-exclusion condition.On the contrary,the NAR numbers are rapidly diminished from 6-9 to below one in the same case with the outlier exclusion condition.Thus,the Box-Cox transformation based on the outlier test of the coastal water quality monitoring data that are not comes form the normal distribution,is highly recommended for the suitable statistical estimation and inferences.